RLCF: A collaborative filtering approach based on reinforcement learning with sequential ratings. Issue 3 (3rd July 2017) - Lee, Jungkyu; Oh, Byonghwa; Yang, Jihoon; Park, Unsang
Abstract: We present a novel approach for collaborative filtering, RLCF, that considers the dynamics of user ratings. RLCF is based on reinforcement learning applied to the sequence of ratings. First, we formalize the collaborative filtering problem as a Markov Decision Process. Then, we learn the connection between the temporal sequences of user ratings using Q-learning. Experiments demonstrate the feasibility of our approach and a tight relationship between the past and the current ratings. We also suggest an ensemble learning in RLCF and demonstrate its improved performance.